通过局部层次相关性引导的Mixup提升文本分类层次结构建模效果
LH-Mix: Local Hierarchy Correlation Guided Mixup over Hierarchical Prompt Tuning
- 用分层提示词显式建模父-子关系，结合局部层次结构
- 提出基于层次相关性的新Mixup比例策略，增强兄弟节点隐含关联
- 在三个主流数据集上显著提升分类性能，适合层次文本分类场景
层次文本分类（HTC）旨在为每篇文本分配一个或多个层级标签。现有方法常将结构视为全局层次，导致冗余图结构。为此，引入文本相关的局部层次结构至关重要。然而，现有方法多将局部层次建模为序列，关注显式的父子关系，忽略兄弟/同级关系间的隐含相关性。本文首先将局部层次融入手动深度级提示词，以捕捉父子关系；随后在此提示词调优框架中应用Mixup，增强同级节点间的潜在关联。特别地，提出一种由局部层次相关性指导的新型Mixup比例，有效捕获内在关联。所提出的局部层次Mixup（LH-Mix）模型在三个广泛使用的数据集上表现优异。
原文摘要 · Abstract (English)
Hierarchical text classification (HTC) aims to assign one or more labels in the hierarchy for each text. Many methods represent this structure as a global hierarchy, leading to redundant graph structures. To address this, incorporating a text-specific local hierarchy is essential. However, existing approaches often model this local hierarchy as a sequence, focusing on explicit parent-child relationships while ignoring implicit correlations among sibling/peer relationships. In this paper, we first integrate local hierarchies into a manual depth-level prompt to capture parent-child relationships. We then apply Mixup to this hierarchical prompt tuning scheme to improve the latent correlation within sibling/peer relationships. Notably, we propose a novel Mixup ratio guided by local hierarchy correlation to effectively capture intrinsic correlations. This Local Hierarchy Mixup (LH-Mix) model demonstrates remarkable performance across three widely-used datasets.
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